TY - JOUR
T1 - Large-scale recommender system with compact latent factor model
AU - Liu, Chien-Liang
AU - Wu, Xuan Wei
N1 - Publisher Copyright:
© 2016 Elsevier Ltd
PY - 2016/12/1
Y1 - 2016/12/1
N2 - This work devises a factorization model called compact latent factor model, in which we propose a compact representation to consider query, user and item in the model. The blend of information retrieval and collaborative filtering is a typical setting in many applications. The proposed model can incorporate various features into the model, and this work demonstrates that the proposed model can incorporate context-aware and content-based features to handle context-aware recommendation and cold-start problems, respectively. Besides recommendation accuracy, a critical problem concerning the computational cost emerges in practical situations. To tackle this problem, this work uses a buffer update scheme to allow the proposed model to process data incrementally, and provide a means to use historical data instances. Meanwhile, we use stochastic gradient descent algorithm along with sampling technique to optimize ranking loss, giving a competitive performance while considering scalability and deployment issues. The experimental results indicate that the proposed algorithm outperforms other alternatives on four datasets.
AB - This work devises a factorization model called compact latent factor model, in which we propose a compact representation to consider query, user and item in the model. The blend of information retrieval and collaborative filtering is a typical setting in many applications. The proposed model can incorporate various features into the model, and this work demonstrates that the proposed model can incorporate context-aware and content-based features to handle context-aware recommendation and cold-start problems, respectively. Besides recommendation accuracy, a critical problem concerning the computational cost emerges in practical situations. To tackle this problem, this work uses a buffer update scheme to allow the proposed model to process data incrementally, and provide a means to use historical data instances. Meanwhile, we use stochastic gradient descent algorithm along with sampling technique to optimize ranking loss, giving a competitive performance while considering scalability and deployment issues. The experimental results indicate that the proposed algorithm outperforms other alternatives on four datasets.
KW - Collaborative filtering
KW - Content-based
KW - Latent factor model
KW - Recommender system
UR - http://www.scopus.com/inward/record.url?scp=84984802051&partnerID=8YFLogxK
U2 - 10.1016/j.eswa.2016.08.009
DO - 10.1016/j.eswa.2016.08.009
M3 - Article
AN - SCOPUS:84984802051
SN - 0957-4174
VL - 64
SP - 467
EP - 475
JO - Expert Systems with Applications
JF - Expert Systems with Applications
ER -